The present invention relates to connectionist expert systems and in particular to a decision support system especially implemented in a data processing system having an algorithm based architecture including artificial neural net models.
2. Description of the Prior Art
There are three fundamental functions of a decision support system: (1) Monitor and Control, (2) Decision Support, and (3) Data Offer.
Monitor and Control comprises system analysis on the basis of a predetermined model of the data available to the system over a period of time. The model is generated by an expert who is knowledgeable of the operation of the object or target of the support system. The result of the analysis and whether there is conformity to the model are communicated, if necessary, to a monitor so that any actions to the target being monitored may be precise and timed. Routine actions (or controls) requiring no monitor decision may be incorporated as functions of the processing system.
Modeling Support comprises using the data obtained over the period of time to conform and refine the model (or hypothesis). These functions may be executed under real time circumstances equivalent to the time scale of Monitor and Control or under less stringent time scale circumstances. The model thus generated by the analysis over a longer period of time may be used as an element of the monitoring function under the real time circumstances.
Data Offer comprises offering the data stored over a period of time in response to a request of the data analyzer or the decider who owns the model. The usual requesting manner includes specifying the data to be retrieved on the basis of various indexes. However, it is conceivable that there can be other methods of presenting sample data to determine the approximate data.
The present invention relates to a data processing system including an algorithm architecture for realizing these functions.
Some known representative decision support systems for industrial process control and business analysis can be briefly summarized, along with their practical problems, for a better understanding of the applications of the subject invention.
(1) A Plant Control System:
Computer control technology has drastically promoted plant automation. It is now practical that steady states can be held by computer control alone. However, for most situations involving unsteady states such as the disposal of an abnormal situation, or the start, stop or variation of a plant run, the decisions of expert operators are still necessary.
As the opportunities for having the computer system control the plant run continue to increase, the plant scale which must be covered by the computer system grows much larger. This scale can be approximately estimated in terms of the number of inputs/outputs to the computer system, which number is frequently as large as several thousands in recent years. As a result, the number of unsteady state plant run portions which are too difficult for automation and which must be entrusted to the decisions of the operators is also continually increasing. How to support the operator in the unsteady states and how to educate an operator capable of making the proper decisions in the unsteady states have become significant industry needs.
For these situations, it is the overall objective of a supplier of the system control technology to prepare an expert model for the plant run and to manipulate the model in accordance with occurring experiences, by effectively analyzing the causes of system symptoms and deciding the correct running methods, or to provide a computer environment for the plant capable of executing the necessary decisions. Here, the model does not indicate any numerical equation derived from prior experiences, but more general expressions (such as rules or frames), i.e., a knowledge engineering model. Attempts to express successful management models for running the plant with such knowledge engineering models have already been made in chemical plants and power plants. However, such attempts still remain at the trial and error stage so that a methodology and operating algorithm for developing and implementing expert models that can practically approach real phenomenon are still desired.
(2) A Dealing System:
Dealing systems, as in the trading of stocks, involve prices which may vary quickly and which have to be grasped to deal the stocks properly and successfully. As the variety of dealing continues to grow and expand globally, the experts for this business have become very rare so that how to educate or secure them is an important interest in this field.
In stock dealing, it is important to identify the experts who are capable of instantly deciding how to deal with the fluctuations in the stock prices. One manual providing a method of reading the stock prices, called the "Point & Figure", is available but has not reached yet the stage at which it can be utilized in real time.
The benefits of an expert with long experiences in stock dealing are desired to be introduced as the knowledge base into a computer, but it remains unsolved how to relate the actual stock price fluctuations to the necessary knowledge, or what useful form the fragmentary knowledge is to be integrated into.
Despite the awareness of the need to implement computer systems capable of making the kinds of decisions mentioned above in industrial control or business on a real time basis, such systems have not been achieved. However, the mass data required to make the decisions is being gathered so that the system decisions could be raised to a higher level by integrating all the data properly. Despite this fact, this concept is not yet embodied because the data integrating means, i.e., the technology of modeling the real phenomenon fails to follow. In particular, the knowledge engineering approach proposes a concept of breaking the limitations of the traditional modeling methods, but still stops in an undeveloped state relating to the correspondence to the real phenomenon, so that further investigations are earnestly desired.